In this paper, a new compressive sampling-based image coding scheme is developed to achieve competitive coding efficiency at lower encoder computational complexity, while supporting error resilience. This technique is particularly suitable for visual communication with resource-deficient devices. At the encoder, compact image representation is produced, which is a polyphase down-sampled version of the input image; but the conventional low-pass filter prior to down-sampling is replaced by a local random binary convolution kernel. The pixels of the resulting down-sampled pre-filtered image are local random measurements and placed in the original spatial configuration. The advantages of the local random measurements are two folds: 1) preserve high-frequency image features that are otherwise discarded by low-pass filtering and 2) remain a conventional image and can therefore be coded by any standardized codec to remove the statistical redundancy of larger scales. Moreover, measurements generated by different kernels can be considered as the multiple descriptions of the original image and therefore the proposed scheme has the advantage of multiple description coding. At the decoder, a unified sparsity-based soft-decoding technique is developed to recover the original image from received measurements in a framework of compressive sensing. Experimental results demonstrate that the proposed scheme is competitive compared with existing methods, with a unique strength of recovering fine details and sharp edges at low bit-rates.